Abstract
Billard and Diday [2] were the first to present a regression method for interval-value data. De Carvalho et al [5] presented a new approach that incorporated the information contained in the ranges of the intervals and that presented a better performance when compared with the Billard and Diday method. However, both methods do not guarantee that the predicted values of the lower bounds (ŷ Li )
will be lower than the predicted values of the upper bounds (ŷ Ui ). This paper presents two approaches based on regression models with inequality constraints that guarantee the mathematical coherence between the predicted values ŷ Li and ŷ Ui . The performance of these approaches, in relation with the methods proposed by Billard and Diday [2] and De Carvalho et al [2], will be evaluated in framework of Monte Carlo experiments.
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de A. Lima Neto, E., de A.T. de Carvalho, F., Freire, E.S. (2005). Applying Constrained Linear Regression Models to Predict Interval-Valued Data. In: Furbach, U. (eds) KI 2005: Advances in Artificial Intelligence. KI 2005. Lecture Notes in Computer Science(), vol 3698. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551263_9
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DOI: https://doi.org/10.1007/11551263_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28761-2
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